• 경영과학
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• 제32권1호
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• pp.15-33
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• 2015

Under complete information, introducing additional constraints to a portfolio will have a negative impact on performance. However, real-life investments inevitably involve use of error-prone estimations, such as expected stock returns. In addition to the reality of incomplete data, investments of most Korean domestic equity funds are regulated externally by the government, as well as internally, resulting in limited maximum investment allocation to single stocks and risk free assets. This paper presents an investment framework, which takes such real-life situations into account, based on a newly developed portfolio selection model considering realistic constraints under incomplete information. Additionally, we examined the effects of additional constraints on portfolio's performance under incomplete information, taking the well-known Samsung and SK group stocks as performance benchmarks during the period beginning from the launch of each commercial fund, 2005 and 2007 respectively, up to 2013. The empirical study shows that an investment model, built under incomplete information with additional constraints, outperformed a model built without any constraints, and benchmarks, in terms of rate of return, standard deviation of returns, and Sharpe ratio.

• 한국방사선학회논문지
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• 제9권6호
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• pp.417-420
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• 2015

본 연구는 탄소 빔의 분할조사 후 세포생존율 (Surviving Fraction, SF) 값에 따른 Linear-Quadratic model, Incomplete Repair model, Marchese model의 결과값을 비교하기 위해 진행하였다. 탄소 빔을 4fraction까지 조사한 후 얻은 세포생존율 값을 바탕으로 mathematica 프로그램 (ver 9.0)을 이용하여 각각의 모델로 결과값을 얻어 비교해 보았다. 그 결과 즉시 NB1RGB를 시딩한 값은 repair가 감안되지 않은 LQ 모델이 적합하였지만 fraction 시행한 후의 결과값은 오차를 보였다. 따라서 Potentially Lethal Damage Repair (PLDR)과 Sublethal Damage Repair (SLDR)의 발생을 각각 감안한 repair 모델을 이용하여 적합한지 판단하였다. 이를 바탕으로 탄소 빔의 분할 조사 시 LQ 모델에 각각의 repair의 양을 감안한 새로운 회복 관련 모델의 적용 가능성을 보고자 하였다.

• 응용통계연구
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• 제24권2호
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• pp.425-433
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• 2011

Reject inference in credit scoring is a statistical approach to adjust for nonrandom sample bias due to rejected applicants. Function estimation approaches are based on the assumption that rejected applicants are not necessary to be included in the estimation, when the missing data mechanism is missing at random. On the other hand, the density estimation approach by using mixture models indicates that reject inference should include rejected applicants in the model. When mixture models are chosen for reject inference, it is often assumed that data follow a normal distribution. If data include missing values, an application of the normal mixture model to fully observed cases may cause another sample bias due to missing values. We extend reject inference by a multivariate normal mixture model to handle incomplete characteristic variables. A simulation study shows that inclusion of incomplete characteristic variables outperforms the function estimation approaches.

• 대한기계학회논문집A
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• 제29권3호
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• pp.447-454
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• 2005

This paper presents a new paradigm for 3-axis tool path generation based on an incomplete 2-manifold mesh model, namely, an inexact polyhedron. When geometric data is transferred from one system to another system and tessellated for tool path generation, the model does not have any topological data between meshes and facets. In contrast to the existing polyhedral machining approach, the proposed method generates tool paths from an incomplete 2-manifold mesh model. In order to generate gouge-free tool paths, CL-meshes are generated by offsetting boundary edges, boundary vertices, and facets. The CL-meshes are sliced by machining planes and the calculated intersections are sorted, trimmed, and linked. The grid method is used to reduce the computing time when range searching problems arise. The method is fully implemented and verified by machining an incomplete 2-manifold mesh model.

• Journal of the Korean Statistical Society
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• 제26권4호
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• pp.493-505
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• 1997

The analysis of binary data appears to many areas such as statistics, biometrics and econometrics. In many cases, data are often collected in which some observations are incomplete. Assume that the missing covariates are missing at random and the responses are completely observed. A method to Bayesian analysis of the binary regression model with incomplete data is presented. In particular, the desired marginal posterior moments of regression parameter are obtained using Meterpolis algorithm (Metropolis et al. 1953) within Gibbs sampler (Gelfand and Smith, 1990). Also, we compare logit model with probit model using Bayes factor which is approximated by importance sampling method. One example is presented.

• Journal of the Korean Data and Information Science Society
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• 제13권1호
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• pp.139-145
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• 2002

This paper deals with the problem of obtaining the Bayesian predictive density function and the prediction intervals for a future observation and the p-th order statistics of n future observations for the exponential model under the censored sampling with incomplete information.

• 정보기술과데이타베이스저널
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• 제3권2호
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• pp.75-96
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• 1996

This study is intended to compare the effectiveness of the neural network inductive learning model with a vector space model in information retrieval. As a result, searches responding to incomplete queries in the neural network inductive learning model produced a higher precision and recall as compared with searches responding to complete queries in the vector space model. The results show that the hybrid methodology of integrating an inductive learning technique with the neural network model can help solve information retrieval problems that are the results of inconsistent indexing and incomplete queries--problems that have plagued information retrieval effectiveness.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 추계학술대회논문집
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• pp.963-966
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• 2005

Model updating method is known to the area to correct finite element models by the results of the experimental modal analysis. Most common methods in model updating depend on a parametric model of the structure. In this case, the number of parameters is normally smaller than that of modal data obtained from an experiment. In order to overcome this limitation, many researchers are trying to get modal data as many as possible to date. 1 want to name this method multiple modified-system generation method. These Methods consist of direct system modification method and feedback controller method. The direct system modification Is to add a mass or stiffness on the original structure or perturb the boundary conditions. The feedback controller method is to make the closed food system with sensor and actuator so as to get the closed loop modal data. In this paper, we need to focus on the feedback controller method because of its simplicity. Several methods related the feedback controller methods are virtual passive controller (VPC) sensitivity enhancement controller (SEC) and mode decoupling controller (MDC). Among them, we will apply MDC to the model updating problem. MDC has various advantages compared with other controllers, such as VPC and SEC. To begin with, only the target mode can be changed without changing modal property of non-target modes. In addition, it is possible to fix any modes if the number of sensors is equal to that of the system modes. Finally, the required control power to achieve desired change of target mode is always lower than those of other methods such as VPC. However, MDC can make the closed loop system unstable when using incomplete modal data. So we need to take action to avoid undesirable instability from incomplete modal data. In this paper, we address the method to design the unique and robust MDD obtained from incomplete modal data. The associated simulation will be Incorporated to demonstrate the usefulness of this method.

• 한국멀티미디어학회논문지
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• 제19권1호
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• pp.22-29
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• 2016

Hidden Markov Model is one of the most successful and popular tools for modeling real world sequential data. Real world signals come in a variety of shapes and variabilities, among which temporal and spectral ones are the prime targets that the HMM aims at. A new problem that is gaining increasing attention is characterizing missing observations in incomplete data sequences. They are incomplete in that there are holes or omitted measurements. The standard HMM algorithms have been developed for complete data with a measurements at each regular point in time. This paper presents a modified algorithm for a discrete HMM that allows substantial amount of omissions in the input sequence. Basically it is a variant of Baum-Welch which explicitly considers the case of isolated or a number of omissions in succession. The algorithm has been tested on online handwriting samples expressed in direction codes. An extensive set of experiments show that the HMM so modeled are highly flexible showing a consistent and robust performance regardless of the amount of omissions.

• Journal of Animal Science and Technology
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• 제47권2호
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• pp.147-158
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• 2005

The purpose of this study was to find out the effects that daughters' incomplete lactation records affect sire's breeding values through genetic evaluation using RRTDM(random regression test-day model). First, we estimated genetic parameters and breeding values on sires having complete lactation records of daughter by RRTDM, second, we changed complete lactation records of specific sires into incomplete records by various methods. Third, the breeding values were compared between complete and incomplete records. Finally, this study aimed to find out the methods to minimize the estimation errors of young bulls' breeding values. Data used in this study were collected from the dairy herd improvement program, and a total of 97,562 records were composed of 10,929 first parity with both parents known, since 1999. Breeding values on the daughters from randomly chosen sires were calculated and compared with among 90 day, 150day, and 200 day's incomplete records. For milk yields, sire's ranks of breeding values used by complete lactation records were very different from sire's ranks of breeding values obtained by incomplete lactation records(Rank_90 cut, 150cut, 200 cut).The differences were also obtained between complete lactation records(per305_full) and incomplete lactation record (per_90 cut, 150cut, 200 cut) in breeding values regarding persistency. Especially, the differences between per_90 cut and per305_full were very large(from 1.8 kg to 145kg).